What you can log and display#
Neptune supports logging of various types of metadata.
The display options in the Neptune app depend on a couple of things:
- The data type and format
- The method you use for logging it to Neptune
- Parameters and model configuration – save single values or organize them in a dictionary structure of your choice.
- Metrics – log metrics and losses as series of values and visualize them as charts.
- Model checkpoints – upload model checkpoint files.
Artifacts and data versioning#
You can generally log metadata about any file with the
track_files() method. This is the handiest way to track and version artifacts that you store elsewhere, such as datasets and model files.
You should only use
upload() for files that you specifically want to view and interact with in Neptune.
Data Version Control (DVC)#
To display DVC files in Neptune, specify which DVC files you would like to log when creating a run.
For example, to log all the DVC files from your working directory and all subdirectories, pass
**/*.dvc to the
Uploading files – how to generally upload any files from disk.
You can upload and display a variety of file formats.
For details, see:
- Images – how to log images one by one or in series.
- Interactive visualizations – such as Matplotlib figures.
- HTML – log from a HTML string object, or upload a file directly.
- Arrays and tensors – log and display as images.
- Tabular data – log and preview CSV or pandas DataFrames.
- Audio and video – log and watch or listen in Neptune.
- Text – how to log text entries in various ways.
The following is automatically logged in the
entrypoint: filename of the script you executed, like
files: the content of the script you executed
For details, see Source code logging.
Neptune supports notebook snapshotting for JupyterLab and Jupyter Notebook with the help of an extension.
For setup instructions, see Working with Jupyter.
If you're running a Jupyter notebook locally – for example, in Visual Studio Code – you can capture the notebook contents by passing the path of the notebook to the
source_code argument of the
Netpune can't snapshot the code you execute in cloud environments, such as Google Colab.
For details, see Logging source code: Jupyter notebooks.
If you have Git initialized in your project directory, Neptune extracts some information from the
.git directory and logs it under the
For details, see What is logged automatically: Git information.
Neptune logs certain system metrics, such as hardware consumption, automatically.
For details, see System metrics.
Date and time#
You can track dates and times by assigning a Python datetime object to a field of your choice.
from datetime import datetime # Record exact end of training run["train/end"] = datetime.now() # Record when evaluation starterd run["eval/start"] = datetime.now() # Other other date metadata run["dataset/creation_date"] = datetime.fromisoformat("1998-11-01")